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# A Guide to NumPy/SciPy Documentation

Note

For an accompanying example, see example.py.

When using Sphinx in combination with the numpy conventions, you should use the numpydoc extension so that your docstrings will be handled correctly. For example, Sphinx will extract the Parameters section from your docstring and convert it into a field list. Using numpydoc will also avoid the reStructuredText errors produced by plain Sphinx when it encounters numpy docstring conventions like section headers (e.g. -------------) that sphinx does not expect to find in docstrings.

It is available from:

Details of how to use it can be found here and here

## Overview

In general, we follow the standard Python style conventions as described here:
Additional PEPs of interest regarding documentation of code:
Use a code checker:

The following import conventions are used throughout the NumPy source and documentation:

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

Do not abbreviate scipy. There is no motivating use case to abbreviate it in the real world, so we avoid it in the documentation to avoid confusion.

It is not necessary to do import numpy as np at the beginning of an example. However, some sub-modules, such as fft, are not imported by default, and you have to include them explicitly:

import numpy.fft

after which you may use it:

np.fft.fft2(...)

## Docstring Standard

A documentation string (docstring) is a string that describes a module, function, class, or method definition. The docstring is a special attribute of the object (object.__doc__) and, for consistency, is surrounded by triple double quotes, i.e.:

"""This is the form of a docstring.

It can be spread over several lines.

"""

NumPy, SciPy, and the scikits follow a common convention for docstrings that provides for consistency, while also allowing our toolchain to produce well-formatted reference guides. This document describes the current community consensus for such a standard. If you have suggestions for improvements, post them on the numpy-discussion list.

Our docstring standard uses re-structured text (reST) syntax and is rendered using Sphinx (a pre-processor that understands the particular documentation style we are using). While a rich set of markup is available, we limit ourselves to a very basic subset, in order to provide docstrings that are easy to read on text-only terminals.

A guiding principle is that human readers of the text are given precedence over contorting docstrings so our tools produce nice output. Rather than sacrificing the readability of the docstrings, we have written pre-processors to assist Sphinx in its task.

The length of docstring lines should be kept to 75 characters to facilitate reading the docstrings in text terminals.

## Status

We are busy converting existing docstrings to the new format, expanding them where they are lacking, as well as writing new ones for undocumented functions. Volunteers are welcome to join the effort on our new documentation system (see the Documentation Editor and the Developer Zone).

## Sections

The sections of the docstring are:

1. Short summary

A one-line summary that does not use variable names or the function name, e.g.

"""The sum of two numbers.

"""

The function signature is normally found by introspection and displayed by the help function. For some functions (notably those written in C) the signature is not available, so we have to specify it as the first line of the docstring:

"""

The sum of two numbers.

"""
2. Deprecation warning

A section (use if applicable) to warn users that the object is deprecated. Section contents should include:

• In what Numpy version the object was deprecated, and when it will be removed.
• Reason for deprecation if this is useful information (e.g., object is superseded, duplicates functionality found elsewhere, etc.).
• New recommended way of obtaining the same functionality.

This section should use the note Sphinx directive instead of an underlined section header.

.. note:: Deprecated in Numpy 1.6
ndobj_old will be removed in Numpy 2.0, it is replaced by
ndobj_new because the latter works also with array subclasses.
3. Extended summary

A few sentences giving an extended description. This section should be used to clarify functionality, not to discuss implementation detail or background theory, which should rather be explored in the notes section below. You may refer to the parameters and the function name, but parameter descriptions still belong in the parameters section.

4. Parameters

Description of the function arguments, keywords and their respective types.

Parameters
----------
x : type
Description of parameter x.

Enclose variables in single backticks. The colon must be preceded by a space, or omitted if the type is absent.

For the parameter types, be as precise as possible. Below are a few examples of parameters and their types.

Parameters
----------
filename : str
copy : bool
dtype : data-type
iterable : iterable object
shape : int or tuple of int
files : list of str

If it is not necessary to specify a keyword argument, use optional:

x : int, optional

Optional keyword parameters have default values, which are displayed as part of the function signature. They can also be detailed in the description:

Description of parameter x (the default is -1, which implies summation
over all axes).

When a parameter can only assume one of a fixed set of values, those values can be listed in braces, with the default appearing first:

order : {'C', 'F', 'A'}
Description of order.

When two or more input parameters have exactly the same type, shape and description, they can be combined:

x1, x2 : array_like
Input arrays, description of x1, x2.
5. Returns

Explanation of the returned values and their types. Similar to the parameters section, except the name of each return value is optional. The type of each return value is always required:

Returns
-------
int
Description of anonymous integer return value.

If both the name and type are specified, the returns section takes the same form as the parameters section:

Returns
-------
err_code : int
Non-zero value indicates error code, or zero on success.
err_msg : str or None
Human readable error message, or None on success.
6. Other parameters

An optional section used to describe infrequently used parameters. It should only be used if a function has a large number of keyword parameters, to prevent cluttering the parameters section.

7. Raises

An optional section detailing which errors get raised and under what conditions:

Raises
------
LinAlgException
If the matrix is not numerically invertible.

This section should be used judiciously, i.e only for errors that are non-obvious or have a large chance of getting raised.

An optional section used to refer to related code. This section can be very useful, but should be used judiciously. The goal is to direct users to other functions they may not be aware of, or have easy means of discovering (by looking at the module docstring, for example). Routines whose docstrings further explain parameters used by this function are good candidates.

As an example, for numpy.mean we would have:

--------
average : Weighted average

When referring to functions in the same sub-module, no prefix is needed, and the tree is searched upwards for a match.

Prefix functions from other sub-modules appropriately. E.g., whilst documenting the random module, refer to a function in fft by

fft.fft2 : 2-D fast discrete Fourier transform

When referring to an entirely different module:

scipy.random.norm : Random variates, PDFs, etc.

Functions may be listed without descriptions, and this is preferable if the functionality is clear from the function name:

--------
func_a : Function a with its description.
func_b, func_c_, func_d
func_e
9. Notes

An optional section that provides additional information about the code, possibly including a discussion of the algorithm. This section may include mathematical equations, written in LaTeX format:

The FFT is a fast implementation of the discrete Fourier transform:

.. math:: X(e^{j\omega } ) = x(n)e^{ - j\omega n}

Equations can also be typeset underneath the math directive:

The discrete-time Fourier time-convolution property states that

.. math::

x(n) * y(n) \Leftrightarrow X(e^{j\omega } )Y(e^{j\omega } )\\
another equation here

Math can furthermore be used inline, i.e.

The value of :math:\omega is larger than 5.

Variable names are displayed in typewriter font, obtained by using \mathtt{var}:

We square the input parameter alpha to obtain
:math:\mathtt{alpha}^2.

Note that LaTeX is not particularly easy to read, so use equations sparingly.

Images are allowed, but should not be central to the explanation; users viewing the docstring as text must be able to comprehend its meaning without resorting to an image viewer. These additional illustrations are included using:

.. image:: filename

where filename is a path relative to the reference guide source directory.

10. References

References cited in the notes section may be listed here, e.g. if you cited the article below using the text [1]_, include it as in the list as follows:

.. [1] O. McNoleg, "The integration of GIS, remote sensing,
expert systems and adaptive co-kriging for environmental habitat
modelling of the Highland Haggis using object-oriented, fuzzy-logic
and neural-network techniques," Computers & Geosciences, vol. 22,
pp. 585-588, 1996.

which renders as

 [1] O. McNoleg, "The integration of GIS, remote sensing, expert systems and adaptive co-kriging for environmental habitat modelling of the Highland Haggis using object-oriented, fuzzy-logic and neural-network techniques," Computers & Geosciences, vol. 22, pp. 585-588, 1996.

Referencing sources of a temporary nature, like web pages, is discouraged. References are meant to augment the docstring, but should not be required to understand it. References are numbered, starting from one, in the order in which they are cited.

1. Examples

An optional section for examples, using the doctest format. This section is meant to illustrate usage, not to provide a testing framework -- for that, use the tests/ directory. While optional, this section is very strongly encouraged.

When multiple examples are provided, they should be separated by blank lines. Comments explaining the examples should have blank lines both above and below them:

3

Comment explaining the second example

>>> np.add([1, 2], [3, 4])
array([4, 6])

For tests with a result that is random or platform-dependent, mark the output as such:

>>> import numpy.random
>>> np.random.rand(2)
array([ 0.35773152,  0.38568979])  #random

You can run examples as doctests using:

>>> np.test(doctests=True)
>>> np.linalg.test(doctests=True)  # for a single module

In IPython it is also possible to run individual examples simply by copy-pasting them in doctest mode:

In [1]: %doctest_mode
Exception reporting mode: Plain
Doctest mode is: ON
>>> %paste
import numpy.random
np.random.rand(2)
## -- End pasted text --
array([ 0.8519522 ,  0.15492887])

It is not necessary to use the doctest markup <BLANKLINE> to indicate empty lines in the output. Note that the option to run the examples through numpy.test is provided for checking if the examples work, not for making the examples part of the testing framework.

The examples may assume that import numpy as np is executed before the example code in numpy. Additional examples may make use of matplotlib for plotting, but should import it explicitly, e.g., import matplotlib.pyplot as plt.

## Documenting classes

### Class docstring

Use the same sections as outlined above (all except Returns are applicable). The constructor (__init__) should also be documented here, the parameters section of the docstring details the constructors parameters.

An Attributes section, located below the parameters section, may be used to describe class variables:

Attributes
----------
x : float
The X coordinate.
y : float
The Y coordinate.

Attributes that are properties and have their own docstrings can be simply listed by name:

Attributes
----------
real
imag
x : float
The X coordinate
y : float
The Y coordinate

In general, it is not necessary to list class methods. Those that are not part of the public API have names that start with an underscore. In some cases, however, a class may have a great many methods, of which only a few are relevant (e.g., subclasses of ndarray). Then, it becomes useful to have an additional methods section:

class Photo(ndarray):
"""
Array with associated photographic information.

...

Attributes
----------
exposure : float
Exposure in seconds.

Methods
-------
colorspace(c='rgb')
Represent the photo in the given colorspace.
gamma(n=1.0)
Change the photo's gamma exposure.

"""

If it is necessary to explain a private method (use with care!), it can be referred to in the extended summary or the notes. Do not list private methods in the methods section.

Note that self is not listed as the first parameter of methods.

### Method docstrings

Document these as you would any other function. Do not include self in the list of parameters. If a method has an equivalent function (which is the case for many ndarray methods for example), the function docstring should contain the detailed documentation, and the method docstring should refer to it. Only put brief summary and See Also sections in the method docstring.

## Documenting class instances

Instances of classes that are part of the Numpy API (for example np.r_ np,c_, np.index_exp, etc.) may require some care. To give these instances a useful docstring, we do the following:

• Single instance: If only a single instance of a class is exposed, document the class. Examples can use the instance name.
• Multiple instances: If multiple instances are exposed, docstrings for each instance are written and assigned to the instances' __doc__ attributes at run time. The class is documented as usual, and the exposed instances can be mentioned in the Notes and See Also sections.

## Documenting constants

Use the same sections as outlined for functions where applicable:

1. summary
2. extended summary (optional)
4. references (optional)
5. examples (optional)

Docstrings for constants will not be visible in text terminals (constants are of immutable type, so docstrings can not be assigned to them like for for class instances), but will appear in the documentation built with Sphinx.

## Documenting modules

Each module should have a docstring with at least a summary line. Other sections are optional, and should be used in the same order as for documenting functions when they are appropriate:

1. summary
2. extended summary
3. routine listings
5. notes
6. references
7. examples

Routine listings are encouraged, especially for large modules, for which it is hard to get a good overview of all functionality provided by looking at the source file(s) or the __all__ dict.

Note that license and author info, while often included in source files, do not belong in docstrings.

## Other points to keep in mind

• Equations : as discussed in the Notes section above, LaTeX formatting should be kept to a minimum. Often it's possible to show equations as Python code or pseudo-code instead, which is much more readable in a terminal. For inline display use double backticks (like y = np.sin(x)). For display with blank lines above and below, use a double colon and indent the code, like:

end of previous sentence::

y = np.sin(x)
• Notes and Warnings : If there are points in the docstring that deserve special emphasis, the reST directives for a note or warning can be used in the vicinity of the context of the warning (inside a section). Syntax:

.. warning:: Warning text.

.. note:: Note text.

Use these sparingly, as they do not look very good in text terminals and are not often necessary. One situation in which a warning can be useful is for marking a known bug that is not yet fixed.

• Questions and Answers : For general questions on how to write docstrings that are not answered in this document, refer to http://docs.scipy.org/numpy/Questions+Answers/.

• array_like : For functions that take arguments which can have not only a type ndarray, but also types that can be converted to an ndarray (i.e. scalar types, sequence types), those arguments can be documented with type array_like.

## Common reST concepts

For paragraphs, indentation is significant and indicates indentation in the output. New paragraphs are marked with a blank line.

Use italics, bold, and monospace if needed in any explanations (but not for variable names and doctest code or multi-line code). Variable, module, function, and class names should be written between single back-ticks (numpy).

A more extensive example of reST markup can be found in this example document; the quick reference is useful while editing.

Line spacing and indentation are significant and should be carefully followed.

## Conclusion

An example of the format shown here is available. Refer to How to Build API/Reference Documentation on how to use Sphinx to build the manual.

This document itself was written in ReStructuredText, and may be converted to HTML using:

\$ rst2html HOWTO_DOCUMENT.txt HOWTO_DOCUMENT.html
Something went wrong with that request. Please try again.